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  • LeetCode SQL 50 Solution/1211. Queries Quality and Percentage

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Here's a structured `README.md` file for **LeetCode 1211 - Queries Quality and Percentage**, formatted for a GitHub repository:
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```md
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# 📊 Queries Quality and Percentage - LeetCode 1211
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## 📌 Problem Statement
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You are given the **Queries** table, which contains information collected from various queries on a database.
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### Queries Table
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| Column Name | Type |
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| ----------- | ------- |
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| query_name | varchar |
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| result | varchar |
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| position | int |
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| rating | int |
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- The **position** column has values from **1 to 500**.
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- The **rating** column has values from **1 to 5**.
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- **Queries with rating < 3 are considered "poor queries".**
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### Definitions:
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1️⃣ **Query Quality:**
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The **average** of the **ratio** between query rating and its position:
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\[
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\text{quality} = \frac{\sum (\text{rating} / \text{position})}{\text{total queries for that name}}
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\]
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2️⃣ **Poor Query Percentage:**
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The percentage of all queries where **rating < 3**:
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\[
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\text{poor\_query\_percentage} = \left(\frac{\text{count of poor queries}}{\text{total queries}}\right) \times 100
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\]
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---
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## 📊 Example 1:
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### Input:
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**Queries Table**
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| query_name | result | position | rating |
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| ---------- | ---------------- | -------- | ------ |
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| Dog | Golden Retriever | 1 | 5 |
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| Dog | German Shepherd | 2 | 5 |
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| Dog | Mule | 200 | 1 |
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| Cat | Shirazi | 5 | 2 |
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| Cat | Siamese | 3 | 3 |
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| Cat | Sphynx | 7 | 4 |
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### Output:
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| query_name | quality | poor_query_percentage |
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| ---------- | ------- | --------------------- |
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| Dog | 2.50 | 33.33 |
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| Cat | 0.66 | 33.33 |
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### Explanation:
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#### **Dog**
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- **Quality Calculation:**
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\[
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\left( \frac{5}{1} + \frac{5}{2} + \frac{1}{200} \right) \div 3 = 2.50
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\]
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- **Poor Query Percentage:**
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- Poor Queries: **1** (Mule, rating = 1)
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- Total Queries: **3**
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\[
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(1 / 3) \times 100 = 33.33\%
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\]
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#### **Cat**
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- **Quality Calculation:**
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\[
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\left( \frac{2}{5} + \frac{3}{3} + \frac{4}{7} \right) \div 3 = 0.66
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\]
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- **Poor Query Percentage:**
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- Poor Queries: **1** (Shirazi, rating = 2)
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- Total Queries: **3**
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\[
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(1 / 3) \times 100 = 33.33\%
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\]
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---
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## 🖥 SQL Solution
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### 1️⃣ Standard MySQL Query
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#### Explanation:
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- **Calculate quality** using `AVG(rating / position)`.
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- **Count poor queries** using `SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END)`.
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- **Calculate percentage** using `(COUNT of poor queries / total queries) * 100`.
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```sql
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SELECT query_name,
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ROUND(AVG(rating * 1.0 / position), 2) AS quality,
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ROUND(SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS poor_query_percentage
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FROM Queries
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GROUP BY query_name;
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```
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---
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### 📝 Step-by-Step Breakdown:
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1️⃣ **Grouping Queries by `query_name`**
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```sql
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GROUP BY query_name;
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```
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- Ensures calculations are **per query type**.
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2️⃣ **Calculating Query Quality**
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```sql
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ROUND(AVG(rating * 1.0 / position), 2) AS quality
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```
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- **`rating / position`** calculates the ratio.
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- **`AVG(...)`** finds the average across all entries for the query.
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- **Multiplying by `1.0` ensures floating-point division.**
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- **`ROUND(..., 2)` rounds to 2 decimal places**.
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3️⃣ **Calculating Poor Query Percentage**
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```sql
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ROUND(SUM(CASE WHEN rating < 3 THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) AS poor_query_percentage
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```
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- **Counts queries with `rating < 3` using `SUM(CASE WHEN ... THEN 1 ELSE 0 END)`**.
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- **Divides by total queries (`COUNT(*)`) and multiplies by `100`**.
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- **Rounds to 2 decimal places**.
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---
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### 2️⃣ Alternative MySQL Query (Using `IF` Instead of `CASE`)
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```sql
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SELECT query_name,
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ROUND(AVG(rating * 1.0 / position), 2) AS quality,
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ROUND(SUM(IF(rating < 3, 1, 0)) * 100.0 / COUNT(*), 2) AS poor_query_percentage
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FROM Queries
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GROUP BY query_name;
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```
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- **`IF(rating < 3, 1, 0)`** is equivalent to `CASE WHEN rating < 3 THEN 1 ELSE 0 END`.
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---
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## 🐍 Pandas Solution (Python)
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#### Explanation:
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- **Group by `query_name`**.
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- **Calculate query quality** as `rating / position`, then average.
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- **Filter poor queries (`rating < 3`) and compute percentage**.
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```python
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import pandas as pd
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def queries_quality(queries: pd.DataFrame) -> pd.DataFrame:
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# Group by query_name
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grouped = queries.groupby("query_name")
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# Compute Quality
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quality = grouped.apply(lambda x: round((x["rating"] / x["position"]).mean(), 2))
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# Compute Poor Query Percentage
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poor_query_percentage = grouped.apply(lambda x: round((x["rating"] < 3).mean() * 100, 2))
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# Return result
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result = pd.DataFrame({"query_name": quality.index,
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"quality": quality.values,
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"poor_query_percentage": poor_query_percentage.values})
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return result
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```
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---
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## 📁 File Structure
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```
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📂 Queries-Quality
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│── 📜 README.md
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│── 📜 solution.sql
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│── 📜 solution_pandas.py
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│── 📜 test_cases.sql
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```
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---
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## 🔗 Useful Links
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- 📖 [LeetCode Problem](https://leetcode.com/problems/queries-quality-and-percentage/)
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- 📚 [SQL `GROUP BY` Documentation](https://www.w3schools.com/sql/sql_groupby.asp)
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- 🐍 [Pandas GroupBy Documentation](https://pandas.pydata.org/docs/reference/groupby.html)
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```
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### Features of this `README.md`:
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✅ **Clear problem statement with table structure**
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✅ **Examples with detailed calculations**
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✅ **SQL and Pandas solutions with explanations**
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✅ **Alternative SQL query for flexibility**
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✅ **File structure for GitHub organization**
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✅ **Useful reference links**
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Let me know if you'd like any modifications! 🚀

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